Novel data science applications in biomedical research http

Novel data science applications in biomedical research

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http: //www. nytimes. com/2016/06/08/technology/online-searches-can-identify-cancer-victims-study-finds. html

http: //www. nytimes. com/2016/07/21/science/human-connectome-brain-map. html

http: //www. nytimes. com/2016/07/26/science/last-universal-ancestor. html?

A surprisingly specific genetic portrait of the ancestor of all living things has been generated by scientists who say that the likeness sheds considerable light on the mystery of how life first emerged on Earth. This venerable ancestor was a single cell, bacterium like organism. But it has a grand name, or at least an acronym. It is known as Luca, the Last Universal Common Ancestor, and is estimated to have lived some four billion years ago, when Earth was a mere 560 million years old. The new finding sharpens the debate between those who believe life began in some extreme environment, such as in deep sea vents or the flanks of volcanoes, and others who favor more normal settings, such as the “warm little pond” proposed by Darwin. The nature of the earliest ancestor of all living things has long been uncertain because three great domains of life seemed to have no common point of origin. The domains are those of the bacteria, the archaea and the eukaryotes. Archaea are bacteria like organisms but with a different metabolism, and the eukaryotes include all plants and animals. Specialists have recently come to believe that the bacteria and archaea were the two earliest domains, with the eukaryotes emerging later. That opened the way for a group of evolutionary biologists, led by William F. Martin of Heinrich Heine University in Düsseldorf, Germany, to try to discern the nature of the organism from which the bacterial and archaeal domains emerged. Their starting point was the known protein coding genes of bacteria and archaea. Some six million such genes have accumulated over the last 20 years in DNA databanks as scientists with the new decoding machines have deposited gene sequences from thousands of microbes. Genes that do the same thing in a human and a mouse are generally related by common descent from an ancestral gene in the first mammal. So by comparing their sequence of DNA letters, genes can be arranged in evolutionary family trees, a property that enabled Dr. Martin and his colleagues to assign the six million genes to a much smaller number of gene families. Of these, only 355 met their criteria for having probably originated in Luca, the joint ancestor of bacteria and archaea. Genes are adapted to an organism’s environment. So Dr. Martin hoped that by pinpointing the genes likely to have been present in Luca, he would also get a glimpse of where and how Luca lived. “I was flabbergasted at the result, I couldn’t believe it, ” he said. The 355 genes pointed quite precisely to an organism that lived in the conditions found in deep sea vents, the gassy, metal laden, intensely hot plumes caused by seawater interacting with magma erupting through the ocean floor. Deep sea vents are surrounded by exotic life forms and, with their extreme chemistry, have long seemed places where life might have originated. The 355 genes ascribable to Luca include some that metabolize hydrogen as a source of energy as well as a http: //www. nytimes. com/2016/07/26/science/last-universal-ancestor. html? gene for an enzyme called reverse gyrase, found only in microbes that live at extremely high temperatures, Dr. Martin and colleagues reported Monday in Nature Microbiology.

https: //www. technologyreview. com/s/602208/how-an-algorithm-learned-to-identifydepressed-individuals-by-studying-their-instagram/

• • One of the curious things about color is that we associate it with emotions. Intuitively, we tend to link darker, grayer colors with negative moods and brighter, lighter colors with positive ones. Indeed, researchers have found that people suffering from depression prefer darker colors. That raises the fascinating possibility that it might be possible to diagnose depression en masse by analyzing the photos people post to social media sites such as Instagram. But how reliable could such an approach ever be? Today, we get an answer thanks to the work of Andrew Reece at Harvard University in Cambridge, Massachusetts, and Chris Danforth at the University of Vermont in Burlington, who have found significant correlations between the colors in photos posted to Instagram and an individual’s mental health. The link is so strong that the pair suggest that it could be used for early detection of mental illness. The researchers began by sourcing some 500 workers from Amazon’s Mechanical Turk service who also had Instagram accounts. They asked these Turkers to complete a series of questionnaires, including a standard clinical depression survey. They then invited the Turkers to share their Instagram posts for the study. Some 170 Turkers agreed, of whom around 70 were clinically depressed. The survey asked various additional questions about their condition, such as the original date of their diagnosis. The Instagram downloads resulted in a database of over 40, 000 photographs which the team then analyzed, again using crowdsourcing with a different set of Turkers. For each healthy user, the researchers chose the 100 most recent photographs to be rated. For depressed individuals, the researchers chose the 100 photographs posted before their diagnosis. These raters were asked to judge how interesting, likeable, happy, and sad each photo seemed on a scale of 0 to 5. The researchers also evaluated the photographs using objective measures such as the average hue, color saturation, contrast, and so on. This shows how vivid a picture is, for example, and whether it appears gray or faded. They also counted the number of faces in each image using face detection software, on the assumption that faces are a proxy for an individual’s level of social activity. And they assessed the Instagram community’s reaction to each image by counting the number of likes and comments. Armed with this data, the researchers used a machine learning algorithm to spot correlations between depression and image properties. The researchers found that depressed individuals tend to post images that are bluer, grayer, and darker, and receive fewer likes, than those posted by heathy individuals. Instagram offers a wide range of filters that give images a certain character and atmosphere. Depressed individuals had a clear favorite among these. “When depressed participants did employ filters, they most disproportionately favored the ‘Inkwell’ filter, which converts color photographs to black and white images, ” say Reece and Danforth. By contrast, healthy individuals preferred a filter called Valencia, which lightens photographs. An interesting question is how well the algorithm can identify depressed individuals using the images they post on Instagram. So the researchers set it lose on the images posted by 100 individuals and found that the algorithm correctly identified 70 percent of those who were depressed. That’s significantly better than GPs manage when asked to identify